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Reidentifikace automobilů v obraze
 Ohradzanská, Karolína ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
Vehicle re-identification is a helpful technic for tracking and monitoring traffic in various situations. This thesis deals with the issue of re-identification cars in the image to track vehicles using camera systems. Specifically, it focuses on the multi-camera vehicle tracking task from the international AI City Challenge competition. In this work were trained five types of convolutional networks and one transformer model. It investigated how successfully different convolutional networks worked compared to the transformer model in the re-identification task. Several experiments were performed with these networks on several datasets, while the resNeXt model achieved a success rate of up to 86.35~\% on the VeRi dataset. Participation in the AI City Challenge in 2023 required creating a dataset with people for the re-identification task.
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